Using the stem both reduces the image dimension before splitting it up to improve computational efficiency, and further allows for local information about the contextual surroundings of each segment to propagate into its representation. This improves over the stricter patchGAN [1]} which blindly splits the image into a fixed tabular grid, while our new loss offers a more natural choice, dividing it up into semantically-meaningful regions.
